Preference Markets: Organizing Securities Markets for Opinion Surveys with Infinite Scalability

Dahan, Ely; Soukhoroukova, Arina; Spann, Martin

doi:10.2139/ssrn.1031754

Cited by 7 publications

(8 citation statements)

References 44 publications

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“…In 'preference markets' participants engage in securities trading, the price of which represents the degree of preference for a decision option. Participants reveal their own preferences and their expectations of others' preferences, and converge towards an equilibrium that captures the consensus view [25]. This type of IAMs has been mainly applied in corporate settings for the selection of new ideas (see for example [26], [27]).…”

Section: Information Aggregation Marketsmentioning

confidence: 99%

Citizen engagement with information aggregation markets

Bothos

Apostolou

Mentzas

2012

IJEG

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Abstract. Participatory governance entails converting citizens from passive users of administrative decisions into active participants of political procedures. Public institutions and organizations can benefit from methods and tools able to aggregate and interpret information dispersed among citizens. In this paper we explore the use of Information Aggregation Markets (IAMs) for citizen engagement. We identify the benefits of IAMs and explain how markets can be used to aggregate citizens' opinions and views on policy issues. Furthermore we report on two real life cases where we deployed IAMs to assist the decision making processes of public organizations. The positive feedback from participants and decision makers provides empirical evidence on the benefits of IAMs as a method for engaging citizens in public policy decision making.

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Section: Information Aggregation Marketsmentioning

confidence: 99%

Citizen engagement with information aggregation markets

Bothos

Apostolou

Mentzas

2012

IJEG

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“…Four studies consider market‐internal payoff mechanisms for preference and idea markets (Chan et al , ; Soukhoroukova and Spann, ; Dahan et al , ; LaComb et al , ). They generally indicate the advantages of market‐based methods compared with other instruments, such as costs, scalability, and the existence of biases, yet none provides a test of external validity or forecasting accuracy.…”

Section: Second‐generation Prediction Markets With Alternative Payoffmentioning

confidence: 99%

“…A third G2 alternative, similar to the previous measure, uses the final trading price but closes the market at a random point in time (Dahan et al , ). Therefore, T random is between two prespecified points in time, random_start and random_end, and the last price random close can be computed as: …”

Section: Second‐generation Prediction Markets With Alternative Payoffmentioning

confidence: 99%

“…That is, they trade concepts of products to forecast expected market shares. Subsequent research has focused on investigating the costs, scalability, and duration of G2 PMs (Soukhoroukova and Spann, ; Dahan et al , ). Another type of G2 PM pertains to the idea market , which is similar to preference markets in that participants trade concepts to evaluate the success of different product or design alternatives.…”

Section: Introductionmentioning

confidence: 99%

“…In turn, the payoff values of the underlying stock shares cannot be linked to the outcome of the event, so the traditional incentive system breaks down. Alternative payoff mechanisms then base the payoff value on the volume‐weighted average trading prices (LaComb et al , ), the last fixed price (Chan et al , ; Soukhoroukova and Spann, ), or the last fixed price when a market closes at a random point in time (Dahan et al , ). Despite the likely influence of different payoff mechanisms on forecasting accuracy, no empirical or comprehensive conceptual comparisons provide knowledge about the forecasting accuracy of those payoff mechanisms or their effects on participants' trading behavior.…”

Section: Introductionmentioning

confidence: 99%

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Second‐Generation Prediction Markets for Information Aggregation: A Comparison of Payoff Mechanisms

Slamka

Jank

Skiera

2011

Journal of Forecasting

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Initial applications of prediction markets (PMs) indicate that they provide good forecasting instruments in many settings, such as elections, the box office, or product sales. One particular characteristic of these ‘first‐generation’ (G1) PMs is that they link the payoff value of a stock's share to the outcome of an event. Recently, ‘second‐generation’ (G2) PMs have introduced alternative mechanisms to determine payoff values which allow them to be used as preference markets for determining preferences for product concepts or as idea markets for generating and evaluating new product ideas. Three different G2 payoff mechanisms appear in the existing literature, but they have never been compared. This study conceptually and empirically compares the forecasting accuracy of the three G2 payoff mechanisms and investigates their influence on participants' trading behavior. We find that G2 payoff mechanisms perform almost as well as their G1 counterpart, and trading behavior is very similar in both markets (i.e. trading prices and trading volume), except during the very last trading hours of the market. These results indicate that G2 PMs are valid instruments and support their applicability shown in previous studies for developing new product ideas or evaluating new product concepts. Copyright © 2011 John Wiley & Sons, Ltd.

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Beyond Conjoint Analysis: Advances in Preference Measurement

Rao

2013

Applied Conjoint Analysis

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We identify gaps and propose several directions for future research in preference measurement. We structure our argument around a framework that views preference measurement as comprising three interrelated components: (1) the problem that the study is ultimately intended to address; (2) the design of the preference measurement task and the data collection approach; (3) the specification and estimation of a preference model, and the conversion into action. Conjoint analysis is only one special case within this framework. We summarize cutting edge research and identify fruitful directions for future investigations pertaining to the framework's three components and to their integration.1 The authors would like to thank the SEI Center for Advanced Studies in Management at Wharton for partially supporting this research and for supporting the 7 th Triennial Choice Conference held at the Wharton School. The first two authors (session co-chairs) and the third to twelfth authors are listed alphabetically. AbstractWe identify gaps and propose several directions for future research in preference measurement. We structure our argument around a framework that views preference measurement as comprising three inter-related components: 1) the problem that the study is ultimately intended to address; 2) the design of the preference measurement task and the data collection approach; 3) the specification and estimation of a preference model, and the conversion into action. Conjoint analysis is only one special case within this framework. We summarize cutting edge research and identify fruitful directions for future investigations pertaining to the framework's three components and to their integration.

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Preference Markets: Organizing Securities Markets for Opinion Surveys with Infinite Scalability

Cited by 7 publications

References 44 publications

Citizen engagement with information aggregation markets

Citizen engagement with information aggregation markets

Second‐Generation Prediction Markets for Information Aggregation: A Comparison of Payoff Mechanisms

Beyond Conjoint Analysis: Advances in Preference Measurement

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